Natural Language Feature Selection via Cooccurrence
This work addresses a specific bottleneck in NLP tasks like collocation extraction and tagging, but it appears incremental as it builds on existing relational data methods without introducing a new paradigm.
The paper tackles the problem of term specificity in natural language processing, where traditional TF-IDF fails to capture semantic relationships, leading to misidentification of general idiomatic terms as specific. The result is a technique that uses relational data to estimate term specificity based on its distribution of relations with other terms.
Specificity is important for extracting collocations, keyphrases, multi-word and index terms [Newman et al. 2012]. It is also useful for tagging, ontology construction [Ryu and Choi 2006], and automatic summarization of documents [Louis and Nenkova 2011, Chali and Hassan 2012]. Term frequency and inverse-document frequency (TF-IDF) are typically used to do this, but fail to take advantage of the semantic relationships between terms [Church and Gale 1995]. The result is that general idiomatic terms are mistaken for specific terms. We demonstrate use of relational data for estimation of term specificity. The specificity of a term can be learned from its distribution of relations with other terms. This technique is useful for identifying relevant words or terms for other natural language processing tasks.